Role-Based Programme
RB1578

AI-Powered Quality Assurance

Smarter Quality Systems, Compliance Monitoring & Continuous Improvement

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Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Apply AI across Quality Assurance activities including quality planning, documentation, process assurance, audits, CAPA, change control, and compliance monitoring.
  • Use AI-assisted techniques to analyse SOPs, quality records, audit findings, deviations, process data, and quality-system information more efficiently.
  • Develop structured workflows for document review, quality-risk assessment, process compliance, corrective action, and quality-system improvement.
  • Analyse Quality Assurance data to identify recurring gaps, overdue actions, documentation weaknesses, process risks, and improvement priorities.
  • Apply responsible AI practices covering evidence integrity, traceability, data accuracy, confidentiality, compliance, and human oversight.

Tools covered

Generative AI AssistantsQuality Management System SupportDocument IntelligenceSpreadsheet AnalysisQuality Risk AnalysisAudit SupportCAPA ManagementChange ControlQuality AnalyticsReporting AssistanceWorkflow Automation

Who should attend

  • Quality Assurance Managers
  • Quality Assurance Engineers
  • Quality Assurance Executives
  • Quality Managers
  • Quality System Professionals
  • Quality Compliance Professionals
  • Internal Quality Auditors
  • CAPA Coordinators
  • Document Control Professionals
  • Process Quality Professionals
  • Validation & Quality Professionals
  • Continuous Improvement Professionals
  • Quality Analysts
  • Quality Management Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of Quality Assurance, Quality Management Systems, compliance, or process-quality activities
  • Familiarity with SOPs, audits, CAPA, deviations, quality records, or change control is helpful
  • Basic document and spreadsheet skills
  • Basic awareness of Generative AI is helpful
  • No programming knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, document intelligence, analytics, and automation in Quality Assurance
  • Identifying AI applications across QMS documentation, compliance, audits, CAPA, and reporting
  • Understanding AI assistance versus Quality Assurance professional judgement and accountability
  • Recognising risks related to inaccurate data, unsupported conclusions, and uncontrolled AI outputs
Practical activities
  • Mapping the Quality Assurance lifecycle to AI-assisted activities
  • Identifying repetitive QA tasks suitable for AI support
  • Comparing traditional and AI-assisted Quality Assurance workflows

Scenarios

Process Deviation to CAPA & Quality Assurance Closure

Deviation → AI-Assisted Evidence Review → Risk Assessment → Root-Cause Analysis → CAPA → Effectiveness Verification → Quality Approval → Closure

Participants analyse a simulated process deviation, review supporting evidence, assess quality risk, develop corrective actions, and track the issue through effectiveness verification and formal QA closure.

Quality Management System Data to Assurance Improvement Plan

Audit Findings + Deviations + CAPA Status + Document Control Data → AI Analysis → Recurring Gaps → Priority Risks → Improvement Actions → Management Report

Participants consolidate Quality Assurance information, identify recurring QMS weaknesses and overdue actions, and prepare a management-ready assurance improvement plan with clear ownership and priorities.

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